Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes?
Electronic clinical documentation can be useful for activities such as public health surveillance, quality improvement, and research, but existing methods of de-identification may not provide sufficient protection of patient data. The general-purpose natural language processor MedLEE retains medical concepts while excluding the remaining text so, in addition to processing text into structured data, it may be able provide a secondary benefit of de-identification. Without modifying the system, the authors tested the ability of MedLEE to remove protected health information (PHI) by comparing 100 outpatient clinical notes with the corresponding XML-tagged output. Of 809 instances of PHI, 26 (3.2%) were detected in output as a result of processing and identification errors. However, PHI in the output was highly transformed, much appearing as normalized terms for medical concepts, potentially making re-identification more difficult. The MedLEE processor may be a good enhancement to other de-identification systems, both removing PHI and providing coded data from clinical text.
Morrison
Frances P
FP
Columbia University Department of Biomedical Informatics, New York, NY, USA. frances.morrison@dbmi.columbia.edu
Li
Li
L
Lai
Albert M
AM
Hripcsak
George
G
D003219:Confidentiality; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D011381:Programming Languages